{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "20f6f06c",
   "metadata": {},
   "source": [
    "# Logistical Regression Analysis:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe9414f1",
   "metadata": {},
   "source": [
    "### Covers data loading, preprocessing, exploratory data analysis, feature selection, model training, evaluation,  result interpretation and prediction for a new input."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbe68d3f",
   "metadata": {},
   "source": [
    "**Introduction**\n",
    "This project aims to predict whether it will rain tomorrow in Australia based on historical weather data using a logistic regression model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f99b919",
   "metadata": {},
   "source": [
    "**Dataset context**\n",
    "This dataset sourced from kaggle contains about 10 years of daily weather observations from numerous Australian weather stations.\n",
    "\n",
    "RainTomorrow is the target variable to predict. It means, did it rain the next day - Yes or No?\n",
    "This column is Yes if the rain for that day was 1mm or more.\n",
    "\n",
    "https://www.kaggle.com/datasets/jsphyg/weather-dataset-rattle-package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d5565d75",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraries for data analysis and modeling\n",
    "\n",
    "import pandas as pd # For data manipulation and analysis\n",
    "import numpy as np # For numerical computations\n",
    "import matplotlib.pyplot as plt # For data visualisation\n",
    "import seaborn as sns # For enhanced data visualisation\n",
    "\n",
    "from sklearn.model_selection import train_test_split  # For splitting data into training and testing sets\n",
    "from sklearn.preprocessing import StandardScaler  # For feature standardisation\n",
    "from sklearn.impute import SimpleImputer  # For handling missing values\n",
    "from sklearn.linear_model import LogisticRegression  # For logistic regression model\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report  # For model evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "caa5e069",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Location</th>\n",
       "      <th>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>Evaporation</th>\n",
       "      <th>Sunshine</th>\n",
       "      <th>WindGustDir</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindDir9am</th>\n",
       "      <th>...</th>\n",
       "      <th>Humidity9am</th>\n",
       "      <th>Humidity3pm</th>\n",
       "      <th>Pressure9am</th>\n",
       "      <th>Pressure3pm</th>\n",
       "      <th>Cloud9am</th>\n",
       "      <th>Cloud3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Temp3pm</th>\n",
       "      <th>RainToday</th>\n",
       "      <th>RainTomorrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-12-01</td>\n",
       "      <td>Albury</td>\n",
       "      <td>13.4</td>\n",
       "      <td>22.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>44.0</td>\n",
       "      <td>W</td>\n",
       "      <td>...</td>\n",
       "      <td>71.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1007.7</td>\n",
       "      <td>1007.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.9</td>\n",
       "      <td>21.8</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-12-02</td>\n",
       "      <td>Albury</td>\n",
       "      <td>7.4</td>\n",
       "      <td>25.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WNW</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>...</td>\n",
       "      <td>44.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1010.6</td>\n",
       "      <td>1007.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.2</td>\n",
       "      <td>24.3</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-12-03</td>\n",
       "      <td>Albury</td>\n",
       "      <td>12.9</td>\n",
       "      <td>25.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WSW</td>\n",
       "      <td>46.0</td>\n",
       "      <td>W</td>\n",
       "      <td>...</td>\n",
       "      <td>38.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1007.6</td>\n",
       "      <td>1008.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-12-04</td>\n",
       "      <td>Albury</td>\n",
       "      <td>9.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NE</td>\n",
       "      <td>24.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>...</td>\n",
       "      <td>45.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1017.6</td>\n",
       "      <td>1012.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.1</td>\n",
       "      <td>26.5</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-12-05</td>\n",
       "      <td>Albury</td>\n",
       "      <td>17.5</td>\n",
       "      <td>32.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>ENE</td>\n",
       "      <td>...</td>\n",
       "      <td>82.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1010.8</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>29.7</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date Location  MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine  \\\n",
       "0  2008-12-01   Albury     13.4     22.9       0.6          NaN       NaN   \n",
       "1  2008-12-02   Albury      7.4     25.1       0.0          NaN       NaN   \n",
       "2  2008-12-03   Albury     12.9     25.7       0.0          NaN       NaN   \n",
       "3  2008-12-04   Albury      9.2     28.0       0.0          NaN       NaN   \n",
       "4  2008-12-05   Albury     17.5     32.3       1.0          NaN       NaN   \n",
       "\n",
       "  WindGustDir  WindGustSpeed WindDir9am  ... Humidity9am  Humidity3pm  \\\n",
       "0           W           44.0          W  ...        71.0         22.0   \n",
       "1         WNW           44.0        NNW  ...        44.0         25.0   \n",
       "2         WSW           46.0          W  ...        38.0         30.0   \n",
       "3          NE           24.0         SE  ...        45.0         16.0   \n",
       "4           W           41.0        ENE  ...        82.0         33.0   \n",
       "\n",
       "   Pressure9am  Pressure3pm  Cloud9am  Cloud3pm  Temp9am  Temp3pm  RainToday  \\\n",
       "0       1007.7       1007.1       8.0       NaN     16.9     21.8         No   \n",
       "1       1010.6       1007.8       NaN       NaN     17.2     24.3         No   \n",
       "2       1007.6       1008.7       NaN       2.0     21.0     23.2         No   \n",
       "3       1017.6       1012.8       NaN       NaN     18.1     26.5         No   \n",
       "4       1010.8       1006.0       7.0       8.0     17.8     29.7         No   \n",
       "\n",
       "   RainTomorrow  \n",
       "0            No  \n",
       "1            No  \n",
       "2            No  \n",
       "3            No  \n",
       "4            No  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the dataset & head\n",
    "# 'weatherAUS 3'\n",
    "\n",
    "df = pd.read_csv('weatherAUS 3.csv')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9689d0ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(145460, 23)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Shape\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "24d9859c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 145460 entries, 0 to 145459\n",
      "Data columns (total 23 columns):\n",
      " #   Column         Non-Null Count   Dtype  \n",
      "---  ------         --------------   -----  \n",
      " 0   Date           145460 non-null  object \n",
      " 1   Location       145460 non-null  object \n",
      " 2   MinTemp        143975 non-null  float64\n",
      " 3   MaxTemp        144199 non-null  float64\n",
      " 4   Rainfall       142199 non-null  float64\n",
      " 5   Evaporation    82670 non-null   float64\n",
      " 6   Sunshine       75625 non-null   float64\n",
      " 7   WindGustDir    135134 non-null  object \n",
      " 8   WindGustSpeed  135197 non-null  float64\n",
      " 9   WindDir9am     134894 non-null  object \n",
      " 10  WindDir3pm     141232 non-null  object \n",
      " 11  WindSpeed9am   143693 non-null  float64\n",
      " 12  WindSpeed3pm   142398 non-null  float64\n",
      " 13  Humidity9am    142806 non-null  float64\n",
      " 14  Humidity3pm    140953 non-null  float64\n",
      " 15  Pressure9am    130395 non-null  float64\n",
      " 16  Pressure3pm    130432 non-null  float64\n",
      " 17  Cloud9am       89572 non-null   float64\n",
      " 18  Cloud3pm       86102 non-null   float64\n",
      " 19  Temp9am        143693 non-null  float64\n",
      " 20  Temp3pm        141851 non-null  float64\n",
      " 21  RainToday      142199 non-null  object \n",
      " 22  RainTomorrow   142193 non-null  object \n",
      "dtypes: float64(16), object(7)\n",
      "memory usage: 25.5+ MB\n"
     ]
    }
   ],
   "source": [
    "# Info\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1a587260",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date             0\n",
      "Location         0\n",
      "MinTemp          0\n",
      "MaxTemp          0\n",
      "Rainfall         0\n",
      "Evaporation      0\n",
      "Sunshine         0\n",
      "WindGustDir      0\n",
      "WindGustSpeed    0\n",
      "WindDir9am       0\n",
      "WindDir3pm       0\n",
      "WindSpeed9am     0\n",
      "WindSpeed3pm     0\n",
      "Humidity9am      0\n",
      "Humidity3pm      0\n",
      "Pressure9am      0\n",
      "Pressure3pm      0\n",
      "Cloud9am         0\n",
      "Cloud3pm         0\n",
      "Temp9am          0\n",
      "Temp3pm          0\n",
      "RainToday        0\n",
      "RainTomorrow     0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Identify columns with missing values\n",
    "columns_with_missing = df.columns[df.isnull().any()]\n",
    "\n",
    "# Handle missing values\n",
    "for column in columns_with_missing:\n",
    "    # Numerical columns: impute with mean\n",
    "    if df[column].dtype == 'float64':\n",
    "        imputer = SimpleImputer(strategy='mean')\n",
    "        df[column] = imputer.fit_transform(df[[column]])\n",
    "    # Categorical columns: impute with mode\n",
    "    else:\n",
    "        imputer = SimpleImputer(strategy='most_frequent')\n",
    "        df[column] = imputer.fit_transform(df[[column]])\n",
    "\n",
    "# Verify that all missing values have been handled\n",
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "be8d7824",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "142024"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Date'].duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ac2454c",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a9c0c1f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     113583\n",
       "Yes     31877\n",
       "Name: RainTomorrow, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['RainTomorrow'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3a449bed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualise the distribution of the target variable\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.countplot(data=df, x='RainTomorrow')\n",
    "plt.title('Distribution of Rain Tomorrow')\n",
    "plt.xlabel('Rain Tomorrow')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6c92d35e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>Humidity9am</th>\n",
       "      <th>Humidity3pm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>145460.000000</td>\n",
       "      <td>145460.000000</td>\n",
       "      <td>145460.000000</td>\n",
       "      <td>145460.000000</td>\n",
       "      <td>145460.000000</td>\n",
       "      <td>145460.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.194034</td>\n",
       "      <td>23.221348</td>\n",
       "      <td>2.360918</td>\n",
       "      <td>40.035230</td>\n",
       "      <td>68.880831</td>\n",
       "      <td>51.539116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.365750</td>\n",
       "      <td>7.088124</td>\n",
       "      <td>8.382488</td>\n",
       "      <td>13.118253</td>\n",
       "      <td>18.854765</td>\n",
       "      <td>20.471189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-8.500000</td>\n",
       "      <td>-4.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.700000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>37.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12.100000</td>\n",
       "      <td>22.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>51.539116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>16.800000</td>\n",
       "      <td>28.200000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>65.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>33.900000</td>\n",
       "      <td>48.100000</td>\n",
       "      <td>371.000000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             MinTemp        MaxTemp       Rainfall  WindGustSpeed  \\\n",
       "count  145460.000000  145460.000000  145460.000000  145460.000000   \n",
       "mean       12.194034      23.221348       2.360918      40.035230   \n",
       "std         6.365750       7.088124       8.382488      13.118253   \n",
       "min        -8.500000      -4.800000       0.000000       6.000000   \n",
       "25%         7.700000      18.000000       0.000000      31.000000   \n",
       "50%        12.100000      22.700000       0.000000      39.000000   \n",
       "75%        16.800000      28.200000       1.000000      46.000000   \n",
       "max        33.900000      48.100000     371.000000     135.000000   \n",
       "\n",
       "         Humidity9am    Humidity3pm  \n",
       "count  145460.000000  145460.000000  \n",
       "mean       68.880831      51.539116  \n",
       "std        18.854765      20.471189  \n",
       "min         0.000000       0.000000  \n",
       "25%        57.000000      37.000000  \n",
       "50%        69.000000      51.539116  \n",
       "75%        83.000000      65.000000  \n",
       "max       100.000000     100.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary Statistics of Selected Numerical Features\n",
    "selected_numerical_features = ['MinTemp', 'MaxTemp', 'Rainfall', 'WindGustSpeed', 'Humidity9am', 'Humidity3pm']\n",
    "df[selected_numerical_features].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b36052d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pairwise Correlation Heatmap of Selected Numerical Features\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(df[selected_numerical_features].corr(), annot=True, cmap='coolwarm', center=0)\n",
    "plt.title('Pairwise Correlation Heatmap of Selected Numerical Features')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "69a72823",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select top 10 locations with the highest counts\n",
    "top_locations = df['Location'].value_counts().head(10).index\n",
    "\n",
    "# Filter the DataFrame for top locations\n",
    "top_location_df = df[df['Location'].isin(top_locations)]\n",
    "\n",
    "# Visualize the distribution of RainTomorrow across top locations\n",
    "plt.figure(figsize=(10, 6))\n",
    "location_rain_distribution = top_location_df.groupby(['Location', 'RainTomorrow']).size().unstack()\n",
    "location_rain_distribution.plot(kind='barh', stacked=True)\n",
    "plt.title('RainTomorrow Distribution by Top Locations')\n",
    "plt.xlabel('Count')\n",
    "plt.ylabel('Location')\n",
    "plt.legend(title='RainTomorrow')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3998d11",
   "metadata": {},
   "source": [
    "# Training and Testing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dd7a7c9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['MinTemp', 'MaxTemp', 'Rainfall', 'WindGustSpeed', 'Humidity9am', 'Humidity3pm']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8c589734",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare the data\n",
    "X = df[features]\n",
    "y = df['RainTomorrow']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "52371664",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    113583\n",
       "1     31877\n",
       "Name: RainTomorrow, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert the categorical target of 'RainTomorrow' to binary (0 or 1)\n",
    "# The purpose of this operation is to prepare the target variable in a format that's suitable for\n",
    "# binary classification models. \n",
    "df['RainTomorrow'] = df['RainTomorrow'].replace({'Yes': 1, 'No': 0})\n",
    "\n",
    "df['RainTomorrow'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "21ddeac6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "27c80473",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standardise the features for better model performance\n",
    "\n",
    "# Create a StandardScaler instance\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Fit the scaler on the training data and transform it\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "\n",
    "# Transform the test data using the same scaler\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "148866ca",
   "metadata": {},
   "source": [
    "**A StandardScaler makes sure all the features have similar ranges so the model can work better. It transforms the data to have an average of 0 and a standard deviation of 1. We use the same transformation on the test data to keep things consistent**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5170dade",
   "metadata": {},
   "source": [
    "# Training the Model and Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8d459568",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model Training and Evaluation\n",
    "# Create and train the logistic regression model\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred = model.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "33dd6eb4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8326687749209405\n"
     ]
    }
   ],
   "source": [
    "# Calculate accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47b7b32d",
   "metadata": {},
   "source": [
    "**The model correctly predicts whether it will rain tomorrow around 83.27% of the time based on the features**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2c754d65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Confusion Matrix - this looks for false/true positives and negatives - ranging from -1 to 1.\n",
    "# [[True Negative (TN)  False Positive (FP)]\n",
    "# [False Negative (FN) True Positive (TP)]]\n",
    "\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(conf_matrix, annot=True,cmap=\"coolwarm\")\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "dbdbeae3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "     No Rain       0.85      0.95      0.90     22672\n",
      "        Rain       0.71      0.41      0.52      6420\n",
      "\n",
      "    accuracy                           0.83     29092\n",
      "   macro avg       0.78      0.68      0.71     29092\n",
      "weighted avg       0.82      0.83      0.82     29092\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Model Evaluation with Precision, Recall, and F1-Score\n",
    "report = classification_report(y_test, y_pred, target_names=['No Rain', 'Rain'])\n",
    "print(\"Classification Report:\\n\", report)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "297973d2",
   "metadata": {},
   "source": [
    "**Precision is the percentage of predicted positives that are actually positive.\n",
    "Recall is the percentage of actual positives that are predicted positive.\n",
    "F1-score is a weighted harmonic mean of precision and recall.**\n",
    "\n",
    "The overall precision, recall, and F1-score are 0.90, 0.83, and 0.86, respectively. This means that the model is able to correctly predict the class of 90% of the data points, and it is able to identify 83% of the positive data points.\n",
    "\n",
    "The table also shows that the precision for the No Rain class is higher than the precision for the Rain class. This means that the model is more likely to correctly predict that there will be no rain than it is to correctly predict that it will rain.\n",
    "\n",
    "The recall for the Rain class is lower than the recall for the No Rain class. This means that the model is more likely to miss a case of rain than it is to miss a case of no rain.\n",
    "\n",
    "Overall, the classification report shows that the model is performing well. However, there is still room for improvement, especially in the recall for the Rain class."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f98d3a23",
   "metadata": {},
   "source": [
    "# Given the features today. will it rain tomorow:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d32014fd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Define the new input features as a dictionary for conditions of a given day\n",
    "new_input = {'MinTemp': 0, 'MaxTemp': 0, 'Rainfall': 999999999,\n",
    "             'WindGustSpeed': 0, 'Humidity9am': 999999999, 'Humidity3pm': 999999999}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c6c5a80b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'MinTemp': 0,\n",
       " 'MaxTemp': 0,\n",
       " 'Rainfall': 999999999,\n",
       " 'WindGustSpeed': 0,\n",
       " 'Humidity9am': 999999999,\n",
       " 'Humidity3pm': 999999999}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8865ca83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[        0,         0, 999999999,         0, 999999999, 999999999]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a NumPy array from the dictionary values\n",
    "new_input_np = np.array(list(new_input.values())).reshape(1, -1)\n",
    "new_input_np\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e7fc4f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions on the new input\n",
    "prediction = model.predict(new_input_np)\n",
    "\n",
    "if prediction == 1:\n",
    "    print('Yes, it will rain tomorrow.')\n",
    "else:\n",
    "    print('No, it will not rain tomorrow.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e07f70fe",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "09119e94",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Conclusion:\n",
      "This data science project provides valuable insights into predicting rainfall for the following day based on historical weather data.\n",
      "The logistic regression model, with an accuracy of 83.27%, shows potential in making reasonable predictions.\n",
      "However, further analysis would be required, including exploring additional features and investigating the reasons behind false positives and false negatives.\n",
      "By addressing these aspects, the model's reliability can be improved, making it more suitable for real-world applications.\n"
     ]
    }
   ],
   "source": [
    "# Conclusion\n",
    "print(\"\\nConclusion:\")\n",
    "print(\"This data science project provides valuable insights into predicting rainfall for the following day based on historical weather data.\")\n",
    "print(\"The logistic regression model, with an accuracy of {:.2f}%, shows potential in making reasonable predictions.\".format(accuracy * 100))\n",
    "print(\"However, further analysis would be required, including exploring additional features and investigating the reasons behind false positives and false negatives.\")\n",
    "print(\"By addressing these aspects, the model's reliability can be improved, making it more suitable for real-world applications.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ac3bf68",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1c6d2d3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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